Interpolation of Missing Temperature Data at Meteorological Stations Using P-BSHADE

Abstract

ome climate datasets are incomplete at certain places and times. A novel technique called the point estimation model of Biased Sentinel Hospitals-based Area Disease Estimation (P-BSHADE) is introduced to interpolate missing data in temperature datasets. Effectiveness of the technique was empirically evaluated in terms of an annual temperature dataset from 1950 to 2000 in China. The P-BSHADE technique uses a weighted summation of observed stations to derive unbiased and minimum error variance estimates of missing data. Both the ratio and covariance between stations were used in calculation of these weights. In this way, interpolation of missing data in the temperature dataset was improved, and best linear unbiased estimates (BLUE) were obtained. Using the same dataset, performance of P-BSHADE was compared against three estimators: kriging, inverse distance weighting (IDW), and spatial regression test (SRT). Kriging and IDW assume a homogeneous stochastic field, which may not be the case. SRT employs spatiotemporal data and has the potential to consider temperature nonhomogeneity caused by topographic differences, but has no objective function for the BLUE. Instead, P-BSHADE takes into account geographic spatial autocorrelation and nonhomogeneity, and maximizes an objective function for the BLUE of the target station. In addition to the theoretical advantages of P-BSHADE over the three other methods, case studies for an annual Chinese temperature dataset demonstrate its empirical superiority, except for the SRT from 1950 to 1970.